tbuckley/cabot-search huggingface dataset:
Radiomic and clinical data integration using machine learning predict the efficacy of anti-PD-1 antibodies-based combinational treatment in advanced breast cancer: a multicentered study
Authors: Jianli Zhao, Zhixian Sun, Yunfang Yu, Zhongyu Yuan, Ying Lin, Yujie Tan, Xiaohui Duan, Herui Yao, Ying Wang, Jieqiong Liu
Journal: Journal for ImmunoTherapy of Cancer (2023)

Background Immune checkpoint inhibitors (ICIs)-based therapy, is regarded as one of the major breakthroughs in cancer treatment. However, it is challenging to accurately identify patients who may benefit from ICIs. Current biomarkers for predicting the efficacy of ICIs require pathological slides, and their accuracy is limited. Here we aim to develop a radiomics model that could accurately predict response of ICIs for patients with advanced breast cancer (ABC). Methods Pretreatment contrast-enhanced CT (CECT) image and clinicopathological features of 240 patients with ABC who underwent ICIs-based treatment in three academic hospitals from February 2018 to January 2022 were assigned into a training cohort and an independent validation cohort. For radiomic features extraction, CECT images of patients 1 month prior to ICIs-based therapies were first delineated with regions of interest. Data dimension reduction, feature selection and radiomics model construction were carried out with multilayer perceptron. Combined the radiomics signatures with independent clinicopathological characteristics, the model was integrated by multivariable logistic regression analysis. Results Among the 240 patients, 171 from Sun Yat-sen Memorial Hospital and Sun Yat-sen University Cancer Center were evaluated as a training cohort, while other 69 from Sun Yat-sen University Cancer Center and the First Affiliated Hospital of Sun Yat-sen University were the validation cohort. The area under the curve (AUC) of radiomics model was 0.994 (95% CI: 0.988 to 1.000) in the training and 0.920 (95% CI: 0.824 to 1.000) in the validation set, respectively, which were significantly better than the performance of clinical model (0.672 for training and 0.634 for validation set). The integrated clinical-radiomics model showed increased but not statistical different predictive ability in both the training (AUC=0.997, 95% CI: 0.993 to 1.000) and validation set (AUC=0.961, 95% CI: 0.885 to 1.000) compared with the radiomics model. Furthermore, the radiomics model could divide patients under ICIs-therapies into high-risk and low-risk group with significantly different progression-free survival both in training (HR=2.705, 95% CI: 1.888 to 3.876, p&lt;0.001) and validation set (HR=2.625, 95% CI: 1.506 to 4.574, p=0.001), respectively. Subgroup analyses showed that the radiomics model was not influenced by programmed death-ligand 1 status, tumor metastatic burden or molecular subtype. Conclusions This radiomics model provided an innovative and accurate way that could stratify patients with ABC who may benefit more from ICIs-based therapies.
Mass Cytometry reveals unique phenotypic patterns associated with subclonal diversity and outcomes in multiple myeloma
Authors: Linda B. Baughn, Erik Jessen, Neeraj Sharma, Hongwei Tang, James B. Smadbeck, Mark D. Long, Kathryn Pearce, Matthew Smith, Surendra Dasari, Zohar Sachs, Michael A. Linden, Joselle Cook, A. Keith Stewart, Marta Chesi, Amit Mitra, P. Leif Bergsagel, Brian Van Ness, Shaji K. Kumar
Journal: Blood Cancer Journal (2023)

Multiple myeloma (MM) remains an incurable plasma cell (PC) malignancy. Although it is known that MM tumor cells display extensive intratumoral genetic heterogeneity, an integrated map of the tumor proteomic landscape has not been comprehensively evaluated. We evaluated 49 primary tumor samples from newly diagnosed or relapsed/refractory MM patients by mass cytometry (CyTOF) using 34 antibody targets to characterize the integrated landscape of single-cell cell surface and intracellular signaling proteins. We identified 13 phenotypic meta-clusters across all samples. The abundance of each phenotypic meta-cluster was compared to patient age, sex, treatment response, tumor genetic abnormalities and overall survival. Relative abundance of several of these phenotypic meta-clusters were associated with disease subtypes and clinical behavior. Increased abundance of phenotypic meta-cluster 1, characterized by elevated CD45 and reduced BCL-2 expression, was significantly associated with a favorable treatment response and improved overall survival independent of tumor genetic abnormalities or patient demographic variables. We validated this association using an unrelated gene expression dataset. This study represents the first, large-scale, single-cell protein atlas of primary MM tumors and demonstrates that subclonal protein profiling may be an important determinant of clinical behavior and outcome.
CAR T-cell detection scoping review: an essential biomarker in critical need of standardization
Authors: David P Turicek, Victoria M Giordani, Josquin Moraly, Naomi Taylor, Nirali N Shah
Journal: Journal for ImmunoTherapy of Cancer (2023)

The expansion and persistence of chimeric antigen receptor (CAR) T-cells in patients are associated with response, toxicity, and long-term efficacy. As such, the tools used to detect CAR T-cells following infusion are fundamental for optimizing this therapeutic approach. Nevertheless, despite the critical value of this essential biomarker, there is significant variability in CAR T-cell detection methods as well as the frequency and intervals of testing. Furthermore, heterogeneity in the reporting of quantitative data adds layers of complexity that limit intertrial and interconstruct comparisons. We sought to assess the heterogeneity of CAR T-cell expansion and persistence data in a scoping review using the PRISMA-ScR checklist. Focusing on 21 clinical trials from the USA, featuring a Food and Drug Administration-approved CAR T-cell construct or one of its predecessors, 105 manuscripts were screened and 60 were selected for analysis, based on the inclusion of CAR T-cell expansion and persistence data. Across the array of CAR T-cell constructs, flow cytometry and quantitative PCR were identified as the two primary techniques for detecting CAR T-cells. However, despite apparent uniformity in detection techniques, the specific methods used were highly variable. Detection time points and the number of evaluated time points also ranged markedly and quantitative data were often not reported. To evaluate whether subsequent manuscripts from a trial resolved these issues, we analyzed all subsequent manuscripts reporting on the 21 clinical trials, recording all expansion and persistence data. While additional detection techniques–including droplet digital PCR, NanoString, and single-cell RNA sequencing–were reported in follow-up publications, inconsistencies with respect to detection time points and frequency remained, with a significant amount of quantitative data still not readily available. Our findings highlight the critical need to establish universal standards for reporting on CAR T-cell detection, especially in early phase studies. The current reporting of non-interconvertible metrics and limited provision of quantitative data make cross-trial and cross-CAR T-cell construct comparisons extremely challenging. Establishing a standardized approach for collecting and reporting data is urgently needed and would represent a substantial advancement in the ability to improve outcomes for patients receiving CAR T-cell therapies.
A non-randomized risk-adjusted comparison of lenalidomide + R-CHOP versus R-CHOP for MYC-rearranged DLBCL patients
Authors: A. Vera de Jonge, Erik van Werkhoven, Avinash G. Dinmohamed, Marcel Nijland, Aeilko H. Zwinderman, Patrick M. Bossuyt, Martine S. Veldhuis, Emma G. G. M. Rutten, Rogier Mous, Joost S. P. Vermaat, Yorick Sandberg, Eva de Jongh, Yavuz M. Bilgin, Rinske Boersma, Harry Koene, Marie José Kersten, Daphne de Jong, Martine E. D. Chamuleau
Journal: Blood Cancer Journal (2023)

Patients with MYC rearranged (MYC-R) diffuse large B-cell lymphoma (DLBCL) have a poor prognosis. Previously, we demonstrated in a single-arm phase II trial (HOVON-130) that addition of lenalidomide to R-CHOP (R2CHOP) is well-tolerated and yields similar complete metabolic remission rates as more intensive chemotherapy regimens in literature. In parallel with this single-arm interventional trial, a prospective observational screening cohort (HOVON-900) was open in which we identified all newly diagnosed MYC-R DLBCL patients in the Netherlands. Eligible patients from the observational cohort that were not included in the interventional trial served as control group in the present risk-adjusted comparison. R2CHOP treated patients from the interventional trial (n = 77) were younger than patients in the R-CHOP control cohort (n = 56) (median age 63 versus 70 years, p = 0.018) and they were more likely to have a lower WHO performance score (p = 0.013). We adjusted for differences at baseline using 1:1 matching, multivariable analysis, and weighting using the propensity score to reduce treatment-selection bias. These analyses consistently showed improved outcome after R2CHOP with HRs of 0.53, 0.51, and 0.59, respectively, for OS, and 0.53, 0.59, and 0.60 for PFS. Thus, this non-randomized risk-adjusted comparison supports R2CHOP as an additional treatment option for MYC-R DLBCL patients.
Genomic Insights into Adaptation to Karst Limestone and Incipient Speciation in East Asian <i>Platycarya</i> spp. (Juglandaceae)
Authors: Yu Cao, Fabricio Almeida-Silva, Wei-Ping Zhang, Ya-Mei Ding, Dan Bai, Wei-Ning Bai, Bo-Wen Zhang, Yves Van de Peer, Da-Yong Zhang
Journal: Molecular Biology and Evolution (2023)

Abstract When challenged by similar environmental conditions, phylogenetically distant taxa often independently evolve similar traits (convergent evolution). Meanwhile, adaptation to extreme habitats might lead to divergence between taxa that are otherwise closely related. These processes have long existed in the conceptual sphere, yet molecular evidence, especially for woody perennials, is scarce. The karst endemic Platycarya longipes and its only congeneric species, Platycarya strobilacea, which is widely distributed in the mountains in East Asia, provide an ideal model for examining the molecular basis of both convergent evolution and speciation. Using chromosome-level genome assemblies of both species, and whole-genome resequencing data from 207 individuals spanning their entire distribution range, we demonstrate that P. longipes and P. strobilacea form two species-specific clades, which diverged around 2.09 million years ago. We find an excess of genomic regions exhibiting extreme interspecific differentiation, potentially due to long-term selection in P. longipes, likely contributing to the incipient speciation of the genus Platycarya. Interestingly, our results unveil underlying karst adaptation in both copies of the calcium influx channel gene TPC1 in P. longipes. TPC1 has previously been identified as a selective target in certain karst-endemic herbs, indicating a convergent adaptation to high calcium stress among karst-endemic species. Our study reveals the genic convergence of TPC1 among karst endemics and the driving forces underneath the incipient speciation of the two Platycarya lineages.
RNA analysis of tape strips to rule out melanoma in lesions clinically assessed as cutaneous malignant melanoma: A diagnostic study
Authors: Ida M. Heerfordt, Peter A. Philipsen, Jeppe D. Andersen, Linnea Langhans, Grethe Schmidt, Niels Morling, Hans Christian Wulf
Journal: Journal of the American Academy of Dermatology (2023)

Distinguishing cutaneous malignant melanoma (CMM) from nevi can be clinically challenging. Suspicious lesions are therefore excised, resulting in many benign lesions being removed surgically to find 1 CMM. It has been proposed to use tape strip derived ribonucleic acid (RNA) to distinguish CMM from nevi.To develop this technique further and validate if RNA profiles can rule out CMM in clinically suspicious lesions with 100% sensitivity.Before surgical excision, 200 lesions clinically assessed as CMM were tape stripped. Expression levels of 11 genes on the tapes were investigated by RNA measurement and used in a rule-out test.Histopathology showed that 73 CMMs and 127 non-CMMs were included. Our test correctly identified all CMMs (100% sensitivity) based on the expression levels of 2 oncogenes, PRAME and KIT, relative to a housekeeping gene. Patient age and sample storage time were also significant. Simultaneously, our test correctly excluded CMM in 32% of non-CMM lesions (32% specificity).Our sample contained a very high proportion of CMMs, perhaps due to inclusion during COVID-19 shutdown. Validation in a separate trial must be performed.Our results demonstrate that the technique can reduce removal of benign lesions by one-third without overlooking any CMMs.
Improved Outcomes After Pulmonary Valve Replacement in Repaired Tetralogy of Fallot
Authors: Jouke P. Bokma, Tal Geva, Lynn A. Sleeper, Ji Hae Lee, Minmin Lu, Tehila Sompolinsky, Sonya V. Babu-Narayan, Rachel M. Wald, Barbara J.M. Mulder, Anne Marie Valente
Journal: Journal of the American College of Cardiology (2023)

The impact of pulmonary valve replacement (PVR) on major adverse clinical outcomes in patients with repaired tetralogy of Fallot (rTOF) is unknown. The purpose of this study was to determine whether PVR is associated with improved survival and freedom from sustained ventricular tachycardia (VT) in rTOF. A PVR propensity score was created to adjust for baseline differences between PVR and non-PVR patients enrolled in INDICATOR (International Multicenter TOF Registry). The primary outcome was time to the earliest occurrence of death or sustained VT. PVR and non-PVR patients were matched 1:1 on PVR propensity score (matched cohort) and in the full cohort, modeling was performed with propensity score as a covariate adjustment. Among 1,143 patients with rTOF (age 27 ± 14 years, 47% PVR, follow-up 8.3 ± 5.2 years), the primary outcome occurred in 82. The adjusted HR for the primary outcome for PVR vs no-PVR (matched cohort n = 524) was 0.41 (95% CI: 0.21-0.81; multivariable model P = 0.010). Full cohort analysis revealed similar results. Subgroup analysis suggested beneficial effects in patients with advanced right ventricular (RV) dilatation (interaction P = 0.046; full cohort). In patients with RV end-systolic volume index >80 mL/m2, PVR was associated with a lower primary outcome risk (HR: 0.32; 95% CI: 0.16-0.62; P < 0.001). There was no association between PVR and the primary outcome in patients with RV end-systolic volume index ≤80 mL/m2 (HR: 0.86; 95% CI: 0.38-1.92; P = 0.70). Compared with rTOF patients who did not receive PVR, propensity score-matched individuals receiving PVR had lower risk of a composite endpoint of death or sustained VT.
Profiling the immune epigenome across global cattle breeds
Authors: Jessica Powell, Andrea Talenti, Andressa Fisch, Johanneke D. Hemmink, Edith Paxton, Philip Toye, Isabel Santos, Beatriz R. Ferreira, Tim K. Connelley, Liam J. Morrison, James G. D. Prendergast
Journal: Genome biology (2023)

Abstract Background Understanding the variation between well and poorly adapted cattle breeds to local environments and pathogens is essential for breeding cattle with improved climate and disease-resistant phenotypes. Although considerable progress has been made towards identifying genetic differences between breeds, variation at the epigenetic and chromatin levels remains poorly characterized. Here, we generate, sequence and analyse over 150 libraries at base-pair resolution to explore the dynamics of DNA methylation and chromatin accessibility of the bovine immune system across three distinct cattle lineages. Results We find extensive epigenetic divergence between the taurine and indicine cattle breeds across immune cell types, which is linked to the levels of local DNA sequence divergence between the two cattle sub-species. The unique cell type profiles enable the deconvolution of complex cellular mixtures using digital cytometry approaches. Finally, we show distinct sub-categories of CpG islands based on their chromatin and methylation profiles that discriminate between classes of distal and gene proximal islands linked to discrete transcriptional states. Conclusions Our study provides a comprehensive resource of DNA methylation, chromatin accessibility and RNA expression profiles of three diverse cattle populations. The findings have important implications, from understanding how genetic editing across breeds, and consequently regulatory backgrounds, may have distinct impacts to designing effective cattle epigenome-wide association studies in non-European breeds.
Theory and rationale of interpretable all-in-one pattern discovery and disentanglement system
Authors: Andrew K. C. Wong, Pei-Yuan Zhou, Annie E.-S. Lee
Journal: npj Digital Medicine (2023)

Abstract In machine learning (ML), association patterns in the data, paths in decision trees, and weights between layers of the neural network are often entangled due to multiple underlying causes, thus masking the pattern-to-source relation, weakening prediction, and defying explanation. This paper presents a revolutionary ML paradigm: pattern discovery and disentanglement (PDD) that disentangles associations and provides an all-in-one knowledge system capable of (a) disentangling patterns to associate with distinct primary sources; (b) discovering rare/imbalanced groups, detecting anomalies and rectifying discrepancies to improve class association, pattern and entity clustering; and (c) organizing knowledge for statistically supported interpretability for causal exploration. Results from case studies have validated such capabilities. The explainable knowledge reveals pattern-source relations on entities, and underlying factors for causal inference, and clinical study and practice; thus, addressing the major concern of interpretability, trust, and reliability when applying ML to healthcare, which is a step towards closing the AI chasm.
In silico methods for predicting functional synonymous variants
Authors: Brian C. Lin, Upendra Katneni, Katarzyna I. Jankowska, Douglas Meyer, Chava Kimchi-Sarfaty
Journal: Genome biology (2023)

Single nucleotide variants (SNVs) contribute to human genomic diversity. Synonymous SNVs are previously considered to be "silent," but mounting evidence has revealed that these variants can cause RNA and protein changes and are implicated in over 85 human diseases and cancers. Recent improvements in computational platforms have led to the development of numerous machine-learning tools, which can be used to advance synonymous SNV research. In this review, we discuss tools that should be used to investigate synonymous variants. We provide supportive examples from seminal studies that demonstrate how these tools have driven new discoveries of functional synonymous SNVs.
Alzheimer's disease: Molecular aspects and treatment opportunities using herbal drugs
Authors: Samridhi Thakral, Alka Yadav, Vikramjeet Singh, Manoj Kumar, Pradeep Kumar, Rakesh Narang, Kalvatala Sudhakar, Amita Verma, Habibullah Khalilullah, Mariusz Jaremko, Abdul-Hamid Emwas
Journal: Ageing Research Reviews (2023)
Solving the explainable AI conundrum by bridging clinicians’ needs and developers’ goals
Authors: Nadine Bienefeld, Jens Michael Boss, Rahel Lüthy, Dominique Brodbeck, Jan Azzati, Mirco Blaser, Jan Willms, Emanuela Keller
Journal: npj Digital Medicine (2023)

Explainable artificial intelligence (XAI) has emerged as a promising solution for addressing the implementation challenges of AI/ML in healthcare. However, little is known about how developers and clinicians interpret XAI and what conflicting goals and requirements they may have. This paper presents the findings of a longitudinal multi-method study involving 112 developers and clinicians co-designing an XAI solution for a clinical decision support system. Our study identifies three key differences between developer and clinician mental models of XAI, including opposing goals (model interpretability vs. clinical plausibility), different sources of truth (data vs. patient), and the role of exploring new vs. exploiting old knowledge. Based on our findings, we propose design solutions that can help address the XAI conundrum in healthcare, including the use of causal inference models, personalized explanations, and ambidexterity between exploration and exploitation mindsets. Our study highlights the importance of considering the perspectives of both developers and clinicians in the design of XAI systems and provides practical recommendations for improving the effectiveness and usability of XAI in healthcare.
Spatial dysregulation of T follicular helper cells impairs vaccine responses in aging
Authors: Alyssa Silva-Cayetano, Sigrid Fra-Bido, Philippe A. Robert, Silvia Innocentin, Alice R. Burton, Emily M. Watson, Jia Le Lee, Louise M. C. Webb, William S. Foster, Ross C. J. McKenzie, Alexandre Bignon, Ine Vanderleyden, Dominik Alterauge, Julia P. Lemos, Edward J. Carr, Danika L. Hill, Isabella Cinti, Karl Balabanian, Dirk Baumjohann, Marion Espeli, Michael Meyer-Hermann, Alice E. Denton, Michelle A. Linterman
Journal: Nature Immunology (2023)

Abstract The magnitude and quality of the germinal center (GC) response decline with age, resulting in poor vaccine-induced immunity in older individuals. A functional GC requires the co-ordination of multiple cell types across time and space, in particular across its two functionally distinct compartments: the light and dark zones. In aged mice, there is CXCR4-mediated mislocalization of T follicular helper (T FH ) cells to the dark zone and a compressed network of follicular dendritic cells (FDCs) in the light zone. Here we show that T FH cell localization is critical for the quality of the antibody response and for the expansion of the FDC network upon immunization. The smaller GC and compressed FDC network in aged mice were corrected by provision of T FH cells that colocalize with FDCs using CXCR5. This demonstrates that the age-dependent defects in the GC response are reversible and shows that T FH cells support stromal cell responses to vaccines.
Graph construction method impacts variation representation and analyses in a bovine super-pangenome
Authors: Alexander S. Leonard, Danang Crysnanto, Xena M. Mapel, Meenu Bhati, Hubert Pausch
Journal: Genome biology (2023)

Several models and algorithms have been proposed to build pangenomes from multiple input assemblies, but their impact on variant representation, and consequently downstream analyses, is largely unknown.We create multi-species super-pangenomes using pggb, cactus, and minigraph with the Bos taurus taurus reference sequence and eleven haplotype-resolved assemblies from taurine and indicine cattle, bison, yak, and gaur. We recover 221 k nonredundant structural variations (SVs) from the pangenomes, of which 135 k (61%) are common to all three. SVs derived from assembly-based calling show high agreement with the consensus calls from the pangenomes (96%), but validate only a small proportion of variations private to each graph. Pggb and cactus, which also incorporate base-level variation, have approximately 95% exact matches with assembly-derived small variant calls, which significantly improves the edit rate when realigning assemblies compared to minigraph. We use the three pangenomes to investigate 9566 variable number tandem repeats (VNTRs), finding 63% have identical predicted repeat counts in the three graphs, while minigraph can over or underestimate the count given its approximate coordinate system. We examine a highly variable VNTR locus and show that repeat unit copy number impacts the expression of proximal genes and non-coding RNA.Our findings indicate good consensus between the three pangenome methods but also show their individual strengths and weaknesses that need to be considered when analysing different types of variants from multiple input assemblies.